In wireless communication systems, joint estimation of frequency offsets and channel gains is computationally complex. Generally, estimation in modern wireless communication is performed by sending a known template of signal called the training sequences and comparing the received training sequence with known pattern of training at the receiver. However, communication systems with multiple transmit and receive antennas require the estimation of frequency offset and channel matrix with dimension equal to the number of transmit and receive antennas. Cooperative communications or Distributed Multiple-Input Multiple-Output (DMIMO) systems have been emerging as a viable option for energy-efficient wireless networks because of its inherent merits of system coverage extension and capacity enhancement along with combating the limitations related to conventional collocated MIMO. The distributed MIMO architecture may be utilized for relaying the source message to a destination resulting in extension of cell coverage, and QoS enhancement through cooperative communication via virtual antenna array (VAA) structure.
On the other hand, orthogonal frequency division multiplexing (OFDM) is a well-known paradigm to support high data rate communications. Accordingly, DMIMO-OFDM system has emerged as a strong candidate for beyond fourth generation mobile communications. The reliable communication of DMIMO-OFDM system largely depends on estimating channel characteristics and multiple carrier frequency offsets (MCFOs) for each transmit-receive antenna pair. The joint estimation process is difficult and computationally complex in high data rate application in practice.
Cooperative or distributed multi-input multi-output (DMIMO) communication system is a key enabler of small-cell deployment, coverage extension, and capacity enhancement by composing an intelligent network with the wireless collaborative nodes. Orthogonal frequency division multiplexing (OFDM) is a strong paradigm because of its inherent robustness to frequency-selective channel. The benefits of DMIMO-OFDM system are maximized when all channels between the transmit antennas and the receive antennas are perfectly known. Imperfect knowledge of channel state information (CSI) causes reduction of capacity and bit error rate (BER) of DMIMO systems.
Performance of DMIMO systems also largely depends on multiple carrier frequency offsets (MCFOs) resulting from individual oscillator of each distributed transmitting nodes and multiple antenna interference (MAI) between received signals. MAI makes MCFOs estimation more difficult. Hence, the knowledge of MCFOs and channel gains are required for coherent deployment of DMIMO-OFDM systems. In practice, joint estimation process is carried out by two techniques; Blind-based and training sequences (TS) based. Blind estimation does not exploit the knowledge of training symbols, and focus on deterministic or stochastic properties of the system. Hence, it does not provide robust estimation for a scenario like DMIMO systems as the wireless nodes are distributed over a geographical area. In training sequence (TS) based method, the TS may be superimposed with information symbols in order to save the transmission bandwidth. The accuracy in such method severely suffers from the interference of information symbols. In contrast, TS and information bearing symbols may be sent in different time slots in time division multiplexing (TDM) mode.
In TDM, the estimation process is dependent on optimal TS design. Accordingly, there is a need to design joint optimal training sequence (TS) for estimating spatially correlated channel characteristics and multiple carrier frequency offsets (MCFOs) in DMIMO-OFDM system in association with its method of generation and apparatus.
For existing documents related to training sequence design for joint channel and frequency offsets estimation in wireless system, reference is made to a non-patent literature “Optimized Training Sequences for Spatially Correlated MIMO-OFDM”—Hoang D. and et. al., IEEE Trans. on Wireless Commun., vol. 9, no. 9, pp. 2768-2778, 2010, wherein collocated MIMO-OFDM system is considered and optimization criterion is based on particular channel estimator (MMSE).
Reference is also made to document, “Optimal Training Design for Channel Estimation in Decode-and-Forward Relay Networks with Individual and Total Power Constraints”—Feifei Gao and et. al., IEEE Trans. on Signal Process. vol. 56, no. 12, pp. 5937-5949, 2008, wherein spatial correlation has not taken into account and optimization criterion is based on minimization of particular estimator's performance (maximum likelihood (ML) and MMSE).
Reference is also made to document, “Optimal Superimposed Training Design for Spatially Correlated Fading MIMO Channels” IEEE Trans on Wireless Commun. vol. 7, no. 8, pp. 3206-3217, 2008, wherein collocated MIMO systems is considered and MMSE channel estimation is the optimization objective function.
Reference is also made to document, “Robust Training Sequence Design for Spatially Correlated MIMO Channel Estimation”—Nafiseh Shariati and et. al., IEEE Trans. on Vehicular Tech. vol. 60, no. 7, pp. 2882-2894, 2011, wherein collocated MIMO system is considered and optimization criterion is based on MMSE channel estimation.
Reference is further made to document, “Joint CFO and Channel Estimation for Multiuser MIMO-OFDM Systems with Optimal Training Sequences”-Jianwu Chen and et. al., IEEE Trans. on Signal Process. vol. 56, no. 8, pp. 4008-4019, 2008, wherein Collocated MIMO system is considered and spatially correlated channel is not considered.
Reference is further made to document US20030016621, entitled “Optimum training sequences for wireless systems” wherein the effect of spatial correlation is not exploited.
Reference is also made to document US20060280266 A1, entitled “Optimum training sequences for wireless systems” wherein spatially correlated channel is not considered.
Reference is also made to document US20040062211 A1, entitled “Assigning training sequences based on spatial channels in a wireless communications system” wherein spatially correlated channel is not considered and joint training sequence design is also not considered.
Reference is further made to document US 20120300644A1, entitled “Method and device for estimating carrier frequency offsets” wherein estimation of frequency offsets is considered while training sequence design is not considered.
To summarize the drawbacks of the prior art: (a) Single parameter estimation method is considered for Collocated MIMO-OFDM system. (b) Optimization criterion is based on particular channel estimator's performance. (c) Spatial correlated channel is not taken into account.
Thus, modern wireless communication approaches towards 5G technology and also demands very high speed energy-efficient communications. Distributed multi-input multi-output (DMIMO) systems reinforce all the need of 5G communications by composing an intelligent network with the wireless collaborative nodes. The coherent deployment of DMIMO systems are based on the proper knowledge of channel state information (CSI), multiple carrier frequency offset (MCFO). Knowledge of such impairments may be obtained by sending a known sequence to the receiver resulting in a need of design of optimal training sequence (TS). The method of training sequence design in this patent provides a spectrally effective way of retrieving CSI and MCFO.
Accordingly, there is a need to develop a system and method for joint optimal Training Sequences design for spatially correlated channel estimation and frequency synchronization of distributed wireless systems that enables a spectrally effective way of jointly retrieving the information of MCFOs and channel gains in DMIMO-OFDM systems.